Gradual Test-Time Adaptation by Self-Training and Style Transfer

Domain shifts at test-time are inevitable in practice. Test-time adaptation addresses this problem by adapting the model during deployment. Recent work theoretically showed that self-training can be a strong method in the setting of gradual domain shifts. In this work we show the natural connection between gradual domain adaptation and test-time adaptation. We publish a new synthetic dataset called CarlaTTA that allows to explore gradual domain shifts during test-time and evaluate several methods in the area of unsupervised domain adaptation and test-time adaptation. We propose a new method GTTA that is based on self-training and style transfer. GTTA explicitly exploits gradual domain shifts and sets a new standard in this area. We further demonstrate the effectiveness of our method on the continual and gradual CIFAR10C, CIFAR100C, and ImageNet-C benchmark.